Sample size recalculation based on the prevalence in a randomized test-treatment study

Abstract
Background: Randomized test-treatment studies aim to evaluate the clinical utility of diagnostic tests by provid-
ing evidence on their impact on patient health. However, the sample size calculation is affected by several factors
involved in the test-treatment pathway, including the prevalence of the disease. Sample size planning is exposed to
strong uncertainties in terms of the necessary assumptions, which have to be compensated for accordingly by adjust-
ing prospectively determined study parameters during the course of the study.
Method: An adaptive design with a blinded sample size recalculation in a randomized test-treatment study based
on the prevalence is proposed and evaluated by a simulation study. The results of the adaptive design are compared
to those of the fixed design.
Results: The adaptive design achieves the desired theoretical power, under the assumption that all other nuisance
parameters have been specified correctly, while wrong assumptions regarding the prevalence may lead to an over- or
underpowered study in the fixed design. The empirical type I error rate is sufficiently controlled in the adaptive design
as well as in the fixed design.
Conclusion: The consideration of a blinded recalculation of the sample size already during the planning of the study
may be advisable in order to increase the possibility of success as well as an enhanced process of the study. However,
the application of the method is subject to a number of limitations associated with the study design in terms of feasi-
bility, sample sizes needed to be achieved, and fulfillment of necessary prerequisites.
Keywords: Adaptive design, Sample size recalculation, Sensitivity, Specificity, Prevalence